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| Registros recuperados: 61 | |
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| OLIVEIRA, D. A. B.; PEREIRA, L. G. R.; BRESOLIN, T.; FERREIRA, R. E. P.; DREA, J. R. R.. |
| In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves mas-sive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such... |
| Tipo: Artigo de periódico |
Palavras-chave: Inteligência artificial; Machine learning; Gado; Agricultura de Precisão; Suíno; Artificial intelligence. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134741 |
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| SILVA, F. C. da; MASSRUHÁ, S. M. F. S.; DEUS, R. S. de; SANTOS, A. D. dos; BARBIERI, V.; CRUZ, S. A. B. da; MALAVOLTA, E.. |
| This paper presents a web-based expert system for diagnosis of plant nutrient disorders in sugarcane. This system aims to provide a guide to identification of essential and functional plant nutrient disorders in sugarcane to avoid deficiency and to solve nutritional problems arising from the development of this culture. It is directed toward the sugarcane farmer, research scientist, extension specialist, student, and consultant. The first version of system was developed using the virtual diagnosis framework developed by Embrapa and CENA/USP. The adopted development methodology and the current status of the system for diagnosis of nutritional deficiency in sugarcane are discussed in this paper. The experience acquired in the development of this expert... |
| Tipo: Artigo de periódico |
Palavras-chave: Sistema especialista para internet; Deficiência nutricional em cana-de-açúcar; Inteligência artificial; Inferência dedutiva; Sugarcane; Expert systems; Artificial intelligence; Saccharum. |
| Ano: 2011 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/897665 |
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| SOUZA, K. X. S. de; OLIVEIRA, S. R. de M.; MACÁRIO, C. G. do N.; ESQUERDO, J. C. D. M.; MOURA, M. F.; LEITE, M. A. de A.; LIMA, H. P. de; CASTRO, A. de; TERNES, S.; YANO, I. H.; SANTOS, E. H. dos. |
| Introdução. Tecnologias digitais. Organização, representação e acesso à informação. Modelagem matemática e estatística. Inteligência artificial. Sensores e estudo da terra. Tecnologias convergentes. Considerações finais. |
| Tipo: Parte de livro |
Palavras-chave: Agricultura digital; Tecnologias digitais; Inteligência artificial; Digital agriculture; Agricultura; Agriculture; Artificial intelligence. |
| Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126215 |
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| OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; MORAES, M. C. B.; MICHEREFF, M. F. F.; AQUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C.. |
| Na publicação: Maria Carolina Blassioli-Moraes. |
| Tipo: Artigo de periódico |
Palavras-chave: Field spectroscopy; Remote sensing; Precision agriculture; Artificial intelligence. |
| Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143293 |
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| Alves,Daniel Pedrosa; Tomaz,Rafael Simões; Laurindo,Bruno Soares; Laurindo,Renata Dias Freitas; Silva,Fabyano Fonseca e; Cruz,Cosme Damião; Nick,Carlos; Silva,Derly José Henriques da. |
| ABSTRACT: Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Phytophthora infestans; ANN; AUDPC; Artificial intelligence; Plant breeding. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100051 |
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| Menezes,Paulo L. de; Azevedo,Carlos A. V. de; Eyng,Eduardo; Dantas Neto,José; Lima,Vera L. A. de. |
| ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs)... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Sprinkler; Water distribution uniformity; Artificial intelligence; Computational model. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662015000900817 |
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| CHIARELLO, F.; STEINER, M. T. A.; OLIVEIRA, E. B. de; ARCE, J. E.; FERREIRA, J. C.. |
| Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to the central nervous system, has gained notoriety and relevance with regard to the classification of standards, intrinsic parameter estimates, remote sense, data mining and other possibilities. This article aims to conduct a systematic review, involving some bibliometric aspects, to detect the application of ANNs in the field of Forest Engineering, particularly in the prognosis of the essential parameters for... |
| Tipo: Artigo de periódico |
Palavras-chave: Bibliometric Review; Multilayer Perceptron; Forest Engineering Problems; Revisão sistemática; Revisão Bibliométrica; Inteligência artificial; Artificial intelligence; Systematic review. |
| Ano: 2019 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115699 |
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| CARVALHO, L. P. de; TEODORO, P. E.; BARROSO, L. M. A.; FARIAS, F. J. C.; MORELLO, C. de L.; NASCIMENTO, M.. |
| Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods,... |
| Tipo: Artigo de periódico |
Palavras-chave: Inteligência artificial; Algodão; Gossypium Hirsutum; Gossypium Hirsutum Marie Galante; Genótipo; Cotton; Artificial intelligence; Genotype-environment interaction. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099791 |
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| SILVA, G. N.; NASCIMENTO, M.; SANT'ANNA, I. de C.; CRUZ, C. D.; CAIXETA, E. T.; CARNEIRO, P. C. S.; ROSADO, R. D. S.; PESTANA, K. N.; ALMEIDA, D. P. de; OLIVEIRA, M. da S.. |
| The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the... |
| Tipo: Artigo de periódico |
Palavras-chave: Inteligência artificial; Predição.; Marcador molecular; Coffea Arábica; Hemileia Vastatrix.; Artificial intelligence; Genetic markers; Prediction.. |
| Ano: 2017 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1069618 |
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| Silva,Gabi Nunes; Nascimento,Moysés; Sant’Anna,Isabela de Castro; Cruz,Cosme Damião; Caixeta,Eveline Teixeira; Carneiro,Pedro Crescêncio Souza; Rosado,Renato Domiciano Silva; Pestana,Kátia Nogueira; Almeida,Dênia Pires de; Oliveira,Marciane da Silva. |
| Abstract: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped,... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Coffea arabica; Hemileia vastatrix; Artificial intelligence; Molecular markers; Prediction. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017000300186 |
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| Abreu,Lucas H. P.; Yanagi Junior,Tadayuki; Bahuti,Marcelo; Hernández-Julio,Yamid F.; Ferraz,Patrícia F. P.. |
| ABSTRACT Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an artificial neural network (ANN) to predict feed conversion (FC), water consumption (Cwater), and cloacal temperature (tclo) of broilers submitted to different air dry-bulb temperatures (24, 27, 30, and 33°C) and durations (1, 2, 3, and 4 days) of thermal stress in the second week of the production cycle. Relative humidity and wind speed were fixed at 60% and 0.2 ms−1, respectively. The experimental data... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Poultry; Thermal stress; Artificial intelligence. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000100001 |
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| Araújo Júnior,Carlos Alberto; Souza,Pábulo Diogo de; Assis,Adriana Leandra de; Cabacinha,Christian Dias; Leite,Helio Garcia; Soares,Carlos Pedro Boechat; Silva,Antonilmar Araújo Lopes da; Castro,Renato Vinícius Oliveira. |
| Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Eucalyptus; Artificial intelligence; Dominant height; Forest inventory; Forest modelling; Non-sampling errors. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000103200 |
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| Ferreira,Mariane Gonçalves; Azevedo,Alcinei Mistico; Siman,Luhan Isaac; da Silva,Gustavo Henrique; Carneiro,Clebson dos Santos; Alves,Flávia Maria; Delazari,Fábio Teixeira; da Silva,Derly José Henriques; Nick,Carlos. |
| ABSTRACT Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Capsicum spp.; Garson’s method; Artificial intelligence; Taxonomy; Germplasm bank. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000300203 |
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| VERSLYPE, N. I.. |
| Os programas de melhoramento genético de plantas buscam selecionar genótipos superiores, para atender as necessidades do homem, no aumento da produtividade, na stabilidade e qualidade das espécies de importância econômica, assim como na redução dos impactos ambientais e nos custos de produção. Dessa forma, a videira (Vitis spp.) é considerada uma fruteira perene de grande importância econômica, social e alimentar. Porém face às mudanças climáticas e a limitação de recursos hídricos, tem havido um crescente investimento no desenvolvimento e uso de porta enxertos tolerantes ao déficit hídrico. No entanto, a obtenção de novas cultivares, tolerantes ao déficit hídrico, trata-se de um processo demorado e difícil por ser uma característica poligênica. Por conta... |
| Tipo: Teses |
Palavras-chave: Algoritmo supervisionado; Algoritmo não supervisionado; Inteligência artificial; Custos de produção; Divergência genética; Uva; Porta Enxerto; Mudança Climática; Melhoramento Vegetal; Impacto Ambiental; Impacto Econômico; Grapes; Plant breeding; Vitis; Artificial intelligence; Algorithms. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1135247 |
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| Celik,Senol; Eyduran,Ecevit; Karadas,Koksal; Tariq,Mohammad Masood. |
| ABSTRACT The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: ANN; Artificial intelligence; Data mining; Decision tree; MARS algorithm. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863 |
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| Registros recuperados: 61 | |
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